OmniToM is a new benchmark for Theory of Mind in LLMs that evaluates explicit belief extraction and seven-dimensional labeling from 895 stories, revealing an actor-specific belief-tracking bottleneck.
Minding language models’ (lack of) theory of mind: A plug-and-play multi-character belief tracker
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UNVERDICTED 4representative citing papers
Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.
AURA improves implicit-need coverage by 0.07 over ReAct baselines on a 100-query benchmark by inserting an intent inference step controlled by a gap score, while cutting probes 82% on factual tasks.
PDDL-Mind improves LLM accuracy on theory-of-mind benchmarks by over 5% by translating stories into verifiable PDDL states that decouple environment tracking from belief inference.
citing papers explorer
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OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
OmniToM is a new benchmark for Theory of Mind in LLMs that evaluates explicit belief extraction and seven-dimensional labeling from 895 stories, revealing an actor-specific belief-tracking bottleneck.
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Bayesian Social Deduction with Graph-Informed Language Models
Hybrid Bayesian-graph LLM agent reaches competitive performance against large models and achieves 67% win rate against humans in controlled Avalon play, outperforming baselines and human teammates.